scholarly journals A simple and novel method for RNA-seq library preparation of single cell cDNA analysis by hyperactive Tn5 transposase

2012 ◽  
Vol 241 (10) ◽  
pp. 1584-1590 ◽  
Author(s):  
Scott Brouilette ◽  
Scott Kuersten ◽  
Charles Mein ◽  
Monika Bozek ◽  
Anna Terry ◽  
...  
Keyword(s):  
Single Cell ◽  
Library Preparation ◽  
Rna Seq ◽  
Cdna Analysis ◽  
Novel Method ◽  
Tn5 Transposase

scholarly journals SHERRY2: A method for rapid and sensitive single cell RNA-seq

2021 ◽  
Author(s):  
Lin Di ◽  
Bo Liu ◽  
Yuzhu Lyu ◽  
Shihui Zhao ◽  
Yuhong Pang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Dynamic Range ◽  
Single Cells ◽  
Rna Seq ◽  
Wide Dynamic Range ◽  
Uniform Coverage ◽  
Optimized Protocol ◽  
Tn5 Transposase ◽  
Higher Sensitivity

Many single cell RNA-seq applications aim to probe a wide dynamic range of gene expression, but most of them are still challenging to accurately quantify low-aboundance transcripts. Based on our previous finding that Tn5 transposase can directly cut-and-tag DNA/RNA hetero-duplexes, we present SHERRY2, an optimized protocol for sequencing transcriptomes of single cells or single nuclei. SHERRY2 is robust and scalable, and it has higher sensitivity and more uniform coverage in comparison with prevalent scRNA-seq methods. With throughput of a few thousand cells per batch, SHERRY2 can reveal the subtle transcriptomic differences between cells and facilitate important biological discoveries.

scholarly journals Fast and accurate single-cell RNA-Seq analysis by clustering of transcript-compatibility counts

2016 ◽  
Author(s):  
Vasilis Ntranos ◽  
Govinda M. Kamath ◽  
Jesse Zhang ◽  
Lior Pachter ◽  
David N. Tse
Keyword(s):  
Single Cell ◽  
Transcriptomic Analysis ◽  
Rna Seq ◽  
Standard Analysis ◽  
Novel Method ◽  
Computationally Intensive ◽  
Or Gene ◽  
Specific Modeling

Current approaches to single-cell transcriptomic analysis are computationally intensive and require assay-specific modeling which limit their scope and generality. We propose a novel method that departs from standard analysis pipelines, comparing and clustering cells based not on their transcript or gene quantifications but on their transcript-compatibility read counts. In re-analysis of two landmark yet disparate single-cell RNA-Seq datasets, we show that our method is up to two orders of magnitude faster than previous approaches, provides accurate and in some cases improved results, and is directly applicable to data from a wide variety of assays.

scholarly journals A novel single-cell based method for breast cancer prognosis

2020 ◽  
Author(s):  
Xiaomei Li ◽  
Lin Liu ◽  
Greg Goodall ◽  
Andreas Schreiber ◽  
Taosheng Xu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Single Cell ◽  
Tumor Heterogeneity ◽  
Breast Cancer Prognosis ◽  
Cancer Prognosis ◽  
Biological Processes ◽  
Expression Data ◽  
Rna Seq ◽  
Novel Method

AbstractBreast cancer prognosis is challenging due to the heterogeneity of the disease. Various computational methods using bulk RNA-seq data have been proposed for breast cancer prognosis. However, these methods suffer from limited performances or ambiguous biological relevance, as a result of the neglect of intra-tumor heterogeneity. Recently, single cell RNA-sequencing (scRNA-seq) has emerged for studying tumor heterogeneity at cellular levels. In this paper, we propose a novel method, scPrognosis, to improve breast cancer prognosis with scRNA-seq data. scPrognosis uses the scRNA-seq data of the biological process Epithelial-to-Mesenchymal Transition (EMT). It firstly infers the EMT pseudotime and a dynamic gene co-expression network, then uses an integrative model to select genes important in EMT based on their expression variation and differentiation in different stages of EMT, and their roles in the dynamic gene co-expression network. To validate and apply the selected signatures to breast cancer prognosis, we use them as the features to build a prediction model with bulk RNA-seq data. The experimental results show that scPrognosis outperforms other benchmark breast cancer prognosis methods that use bulk RNA-seq data. Moreover, the dynamic changes in the expression of the selected signature genes in EMT may provide clues to the link between EMT and clinical outcomes of breast cancer. scPrognosis will also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.Author summaryVarious computational methods have been developed for breast cancer prognosis. However, those methods mainly use the gene expression data generated by the bulk RNA sequencing techniques, which average the expression level of a gene across different cell types. As breast cancer is a heterogenous disease, the bulk gene expression may not be the ideal resource for cancer prognosis. In this study, we propose a novel method to improve breast cancer prognosis using scRNA-seq data. The proposed method has been applied to the EMT scRNA-seq dataset for identifying breast cancer signatures for prognosis. In comparison with existing bulk expression data based methods in breast cancer prognosis, our method shows a better performance. Our single-cell-based signatures provide clues to the relation between EMT and clinical outcomes of breast cancer. In addition, the proposed method can also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.

scholarly journals Cobolt: integrative analysis of multimodal single-cell sequencing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Boying Gong ◽  
Yun Zhou ◽  
Elizabeth Purdom
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Chromatin Accessibility ◽  
Integrative Analysis ◽  
Rna Seq ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Multiple Datasets ◽  
Novel Method ◽  
Sequencing Platforms

AbstractA growing number of single-cell sequencing platforms enable joint profiling of multiple omics from the same cells. We present , a novel method that not only allows for analyzing the data from joint-modality platforms, but provides a coherent framework for the integration of multiple datasets measured on different modalities. We demonstrate its performance on multi-modality data of gene expression and chromatin accessibility and illustrate the integration abilities of by jointly analyzing this multi-modality data with single-cell RNA-seq and ATAC-seq datasets.

A protocol for Targeted Perturb (TAP)-seq and targeted single-cell RNA-seq

2020 ◽  
Author(s):  
Daniel Schraivogel ◽  
Lars Velten ◽  
Andreas R. Gschwind ◽  
Lars M. Steinmetz
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
Illumina Sequencing ◽  
Droplet Formation ◽  
Library Preparation ◽  
Rna Seq ◽  
Cell Preparation

Abstract Here we provide a step-by-step protocol for targeted single-cell RNA-seq and targeted Perturb-seq, as reported in the linked publication Schraivogel et al. Nat Meth 2020. The protocol describes cell preparation using flow cytometry, single-cell droplet formation with 10X Genomics, and targeted 3’ single-cell RNA-seq library preparation for Illumina sequencing.

Comparative analysis of sequencing technologies platforms for single-cell transcriptomics

2018 ◽  
Author(s):  
Kedar Nath Natarajan ◽  
Zhichao Miao ◽  
Miaomiao Jiang ◽  
Xiaoyun Huang ◽  
Hongpo Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
K562 Cells ◽  
Library Preparation ◽  
Rna Seq ◽  
Illumina Hiseq ◽  
Technical Variability ◽  
Sequencing Technologies ◽  
Sequencing Platforms

AbstractAll single-cell RNA-seq protocols and technologies require library preparation prior to sequencing on a platform such as Illumina. Here, we present the first report to utilize the BGISEQ-500 platform for scRNA-seq, and compare the sensitivity and accuracy to Illumina sequencing. We generate a scRNA-seq resource of 468 unique single-cells and 1,297 matched single cDNA samples, performing SMARTer and Smart-seq2 protocols on mESCs and K562 cells with RNA spike-ins. We sequence these libraries on both BGISEQ-500 and Illumina HiSeq platforms using single- and paired-end reads. The two platforms have comparable sensitivity and accuracy in terms of quantification of gene expression, and low technical variability. Our study provides a standardised scRNA-seq resource to benchmark new scRNA-seq library preparation protocols and sequencing platforms.

scholarly journals A systematic evaluation of single cell RNA-seq analysis pipelines

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Beate Vieth ◽  
Swati Parekh ◽  
Christoph Ziegenhain ◽  
Wolfgang Enard ◽  
Ines Hellmann
Keyword(s):  
Best Practices ◽  
Sample Size ◽  
Single Cell ◽  
Differential Expression ◽  
Rna Sequencing ◽  
Systematic Evaluation ◽  
Library Preparation ◽  
Rna Seq ◽  
Rapid Spread ◽  
Single Cell Rna Sequencing

Abstract The recent rapid spread of single cell RNA sequencing (scRNA-seq) methods has created a large variety of experimental and computational pipelines for which best practices have not yet been established. Here, we use simulations based on five scRNA-seq library protocols in combination with nine realistic differential expression (DE) setups to systematically evaluate three mapping, four imputation, seven normalisation and four differential expression testing approaches resulting in ~3000 pipelines, allowing us to also assess interactions among pipeline steps. We find that choices of normalisation and library preparation protocols have the biggest impact on scRNA-seq analyses. Specifically, we find that library preparation determines the ability to detect symmetric expression differences, while normalisation dominates pipeline performance in asymmetric DE-setups. Finally, we illustrate the importance of informed choices by showing that a good scRNA-seq pipeline can have the same impact on detecting a biological signal as quadrupling the sample size.

scholarly journals SC1: A Tool for Interactive Web-Based Single Cell RNA-Seq Data Analysis

2021 ◽  
Author(s):  
Marmar Moussa ◽  
Ion Mandoiu
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Differential Expression Analysis ◽  
Analysis Tool ◽  
Rna Seq ◽  
Web Based ◽  
Inverse Document Frequency ◽  
Sequencing Technologies ◽  
Document Frequency ◽  
Novel Method

Single cell RNA-Seq (scRNA-Seq) is critical for studying cellular function and phenotypic heterogeneity as well as the development of tissues and tumors. Here, we present SC1 a web-based highly interactive scRNA-Seq data analysis tool publicly accessible at https://sc1.engr.uconn.edu. The tool presents an integrated workflow for scRNA-Seq analysis, implements a novel method of selecting informative genes based on Term-Frequency Inverse-Document-Frequency (TF-IDF) scores, and provides a broad range of methods for clustering, differential expression analysis, gene enrichment, interactive visualization, and cell cycle analysis. The tool integrates other single cell omics data modalities like TCR-Seq and supports several single cell sequencing technologies. In just a few steps, researchers can generate a comprehensive analysis and gain powerful insights from their scRNA-Seq data.

scholarly journals Validation of methods for Low-volume RNA-seq

2014 ◽  
Author(s):  
Peter Acuña Combs ◽  
Michael B Eisen
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Critical Evaluation ◽  
Standard Protocol ◽  
Library Preparation ◽  
Rna Seq ◽  
Simple Modification ◽  
Input Sample ◽  
Low Volume ◽  
The Cost

Recently, a number of protocols extending RNA-sequencing to the single-cell regime have been published. However, we were concerned that the additional steps to deal with such minute quantities of input sample would introduce serious biases that would make analysis of the data using existing approaches invalid. In this study, we performed a critical evaluation of several of these low-volume RNA-seq protocols, and found that they performed slightly less well in metrics of interest to us than a more standard protocol, but with at least two orders of magnitude less sample required. We also explored a simple modification to one of these protocols that, for many samples, reduced the cost of library preparation to approximately $20/sample.

scholarly journals TM3’seq: A Tagmentation-Mediated 3’ Sequencing Approach for Improving Scalability of RNAseq Experiments

2019 ◽  
Vol 10 (1) ◽  
pp. 143-150 ◽  
Author(s):  
Luisa F. Pallares ◽  
Serge Picard ◽  
Julien F. Ayroles
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Enriched Library ◽  
Library Preparation ◽  
Rna Seq ◽  
Medical Genomics ◽  
Genome Wide ◽  
Standard Tool ◽  
Commercial Kits ◽  
The Cost

RNA-seq has become the standard tool for collecting genome-wide expression data in diverse fields, from quantitative genetics and medical genomics to ecology and developmental biology. However, RNA-seq library preparation is still prohibitive for many laboratories. Recently, the field of single-cell transcriptomics has reduced costs and increased throughput by adopting early barcoding and pooling of individual samples —producing a single final library containing all samples. In contrast, RNA-seq protocols where each sample is processed individually are significantly more expensive and lower throughput than single-cell approaches. Yet, many projects depend on individual library generation to preserve important samples or for follow-up re-sequencing experiments. Improving on currently available RNA-seq methods we have developed TM3′seq, a 3′-enriched library preparation protocol that uses Tn5 transposase and preserves sample identity at each step. TM3′seq is designed for high-throughput processing of individual samples (96 samples in 6h, with only 3h hands-on time) at a fraction of the cost of commercial kits ($1.5 per sample). The protocol was tested in a range of human and Drosophila melanogaster RNA samples, recovering transcriptomes of the same quality and reliability than the commercial NEBNext kit. We expect that the cost- and time-efficient features of TM3′seq make large-scale RNA-seq experiments more permissive for the entire scientific community.

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